The experiments have shown that training a neural network with a distance-based loss function worked reasonably well for static pose imitation.
Local minima in the distance function and over-fitting on the training set posed some problems. While the former is more difficult to solve, the latter can be solved by increasing the size of the training set and stopping the training process at a suitable time. Another possibility to improve the network's performance may lie in the structure and training of the network. We used the same structure for all pose imitation tasks without employing techniques that decrease the probability of over-fitting, such as dropout or regularization [1]}.
